Transition index maps for urban growth simulation: application of artificial neural networks,weight of evidence and fuzzy multi-criteria evaluation

Transition index maps (TIMs) are key products in urban growth simulation models. However, their operationalization is still conflicting. Our aim was to compare the prediction accuracy of three TIM-based spatially explicit land cover change (LCC) models in the mega city of Mumbai, India. These LCC models include two data-driven approaches, namely artificial neural networks (ANNs) and weight of evidence (WOE), and one knowledge-based approach which integrates an analytical hierarchical process with fuzzy membership functions (FAHP). Using the relative operating characteristics (ROC), the performance of these three LCC models were evaluated. The results showed 85%, 75%, and 73% accuracy for the ANN, FAHP, and WOE. The ANN was clearly superior compared to the other LCC models when simulating urban growth for the year 2010; hence, ANN was used to predict urban growth for 2020 and 2030. Projected urban growth maps were assessed using statistical measures, including figure of merit, average spatial distance deviation, producer accuracy, and overall accuracy. Based on our findings, we recomend ANNs as an and accurate method for simulating future patterns of urban growth.     

Reliable method for prediction of the flash point of various classes of amines on the basis of some molecular moieties for safety measures in industrial processes

This work presents a novel, reliable and simple method of estimating the flash point of various types of flammable amines, which are important for safety measures in industrial processes. Different amines include aliphatic amines such as primary, secondary, tertiary and cyclic amines as well as aromatic amines and hetero arenes containing nitrogen heteroatom. The proposed correlation is based on the contribution of some specific molecular moieties and functional groups, which can easily be used for any types of amines. Intermolecular forces are important in the new method, which are counted by two increasing and decreasing parameters. The root mean square (rms) deviation is 18 K for different classes of amines including 133 diverse compounds. The estimated flash points have been compared with one of the best available predictive methods, which gives much lower value of the rms deviation.     

Daily Precipitation Extremes in Iran: Decadal Anomalies and Possible Drivers

This study focuses on the empirical statistical analysis of the anomalies in daily precipitation extremes by applying the quantile perturbation method (QPM) to data from 31 Iranian weather stations during the period between 1961 and 2005. The possible causes behind the anomalies in precipitation extremes are identified by analyzing their relationship with the anomalies in eight atmospheric indices (i.e., NAO, SOI, PDO, AMO, NCP, DMI, WeMO, SSN). In terms of decadal oscillations, the country was generally wet in the 1960s and 1970s with most stations exhibiting periods of higher quantile perturbations, whereas lower quantile perturbations were dominant in the 1980s and 1990s. The highest perturbation in extreme precipitation quantiles prevails in Central Iran during the early 1980s, in which the quantiles are about 50% higher than the ones based on the full time series. The frequency of significant precipitation anomalies for winter season was greater than that for spring and autumn seasons. For the summer season, the humid region in North Iran demonstrates strong positive anomalies. The results highlight the noticeable role of large‐scale climatic factors in the anomalous behavior of precipitation extremes in Iran. The atmospheric drivers of the quantile anomalies in extreme precipitation were found to differ from one season to another.     

Mathematical Model for Pollution Source Identification in Rivers

Release of pollution into rivers is required to be handled with special consideration to environmental standards. For this purpose, it is essential to specify the contribution of each pollution source in contamination of water resources. In this study, a mathematical model is proposed for determining locations and concentration release histories of polluting point sources using measured downstream river concentrations via an inverse problem framework. The inverse solution is based on the integral equation obtained from applying the Green's function method on the one-dimensional advection-dispersion contaminant transport equation. Discretization of this integral equation results in a linear, over-determined and ill-posed system of algebraic equations that are solved by using the Tikhonov regularization method. Several examples and some real field data are investigated to illustrate the abilities of the proposed model. Results imply that the proposed method is effective and can identify the pollution sources in rivers with acceptable accuracy.     

Analyzing material flow in Alborz industrial estate,Ghazvin, Iran

Journal of Material Cycles and Waste Management - Industrial ecology and models such as material flow analysis make operational approaches in order to reuse produced waste as primary resources in...     

Synergism effects of phenol-degrading yeast and ammonia-oxidizing bacteria for nitrification in coke wastewater of Esfahan Steel Company.

Ammonia-oxidizing bacteria and phenol-degrading yeast were isolated in order to study the synergism effects of phenol-degrading yeast and ammonia-oxidizing bacteria for enhancing the nitrification in coke wastewater from the Isfahan Steel Company. The influent and effluent samples with approximately 600-1200 mg L(-1) ammonium and 550-2350 mg L(-1) phenol were collected aseptically in sterile flasks. The biodegradation of phenol and nitrification were studied with different treatments. The results showed that addition of Na2CO3 and autotrophic bacteria to wastewater increased the ammonium removal by 100%. Furthermore, the synergism effects of phenol-degrader yeast and autotrophic bacteria reduced the time for ammonium removal.     

A novel polyacrylonitrile–zeolite nanocomposite to clean Cs and Sr from radioactive waste

Radioactive wastes containing Cs⁺ and Sr²⁺ are among the most dangerous environmental pollutants. Therefore, removing Cs⁺ and Sr²⁺ from environmental media is needed. Removal can be done by nanocrystalline ion exchangers. Nanocrystalline ion exchangers are studied in depth for the treatment of nuclear wastes because these exchangers have high exchange capacity and fast kinetics. However, operating the columns of these exchangers is very difficult. This issue may be overcome by the preparation and use of nanocomposites. Here, we prepared a novel polyacrylonitrile–zeolite nanocomposite for the removal of Cs⁺ and Sr²⁺ in a fixed-bed column operation. We studied the effect of influent flow rate, nanocomposite bed height and initial concentrations. Experimental data were analysed using the Thomas model and the bed-depth service time model. The results reveal that total adsorbed ion and bed capacity increased with increasing initial ions concentration and bed height; and decreased with increasing influent flow rate. The maximum bed capacity was 0.085 meq/g for Cs⁺ and 0.128 meq/g for Sr²⁺. The critical bed height (Z ₀) was 4.35 cm for Cs⁺ and 2.89 cm for Sr²⁺. These findings demonstrate that the new nanocomposite is suitable for removal of Cs⁺ and Sr²⁺.     

Experimental study of rubber tire aggregates effect on compressive and dynamic load-bearing properties of cylindrical concrete specimens

Sustainable development has become a major focus for engineers and planners as part of their collective efforts in finding, developing and integrating environmental-friendly solutions for material recycling and waste management into design and construction of civil engineering infrastructure. In the past three decades, there has been an increase in recycling and application of waste materials into the concrete to decrease costs and improve material properties of the concrete. Significant growth in automobile manufacturing industry and increased rubber tire supply for vehicles suggested the application of waste tire particles as concrete aggregates to minimize the ecological footprint of the rubber tire waste due to its recycling process difficulties. In this paper, the effect of rubber tire particles on compressive and dynamic strength of concrete specimens with different particle percentiles was tested on more than 55 cylindrical specimens. To achieve the optimal mix design properties of rubber tire concrete specimens, both fine and coarse aggregates got replaced by fine and coarse rubber particles. Introduction of rubber tire particles as coarse and fine aggregate reduces the brittleness of the concrete and provides more flexible aggregate bonding which ultimately improves the dynamic resistance of the concrete. It increases the concrete workability and provides environmental-friendly and cost-effective solutions in using recycled materials for concrete construction applications.